原油常减压蒸馏装置的模拟

用石油馏分的蒸馏曲线(ASTM或TBP)和整体密度进行虚拟组分切割，以单元模块组合法严格求解常减压蒸馏塔，建立原油减压蒸馏装置的流程模拟系统。     

常减压蒸馏装置减压深拔技术的应用

通过介绍中石化安庆分公司500万吨/年常减压蒸馏(Ⅱ)装置的主要减压深拔技术特点、生产过程中的优化调整和减压深拔后的效果,表明在实施减压深拔技术后装置的总拔出率明显提高,蜡油质量合格,原油实沸点切割温度达到了580℃的设计目标。     

原油调合技术在中化泉州石化上的应用分析

本文对中化泉州石化原油调和优化系统进行介绍,结合公司原油调合系统的运行情况,阐述调合系统可以降低原油采购成本,稳定常减压进料性质,改善常减压装置操作,实现原油调合自动化和信息共享,通过炼油企业的计划、调度、操作执行各层面工作整体管理,提升原油处理水平,达到炼油加工效益最大化的目标。     

炼油企业级PIMS模型应用探讨

介绍了中国石化炼油企业级PIMS模型的开发应用情况和主要功能提升情况.举例说明了企业级PIMS模型在炼油生产计划制定、原油采购、新装置开工、装置检修、中间物料、产品结构、产品调合优化等方面的应用情况.     

常减压蒸馏装置的减压深拔设计与实践

某厂8 Mt/a常减压装置采用了LPEC公司自主知识产权的减压深拔技术,实践中通过优化操作条件,实现了较低残压(1.6 kPa)和较低全塔压降(1.33 kPa)下,提高减压炉温,实现了总拔收率对应原油实沸点切割温度达到580℃,渣油538℃馏出≤4%(v),且HVGO产品质量合格的设计要求;装置长期满负荷安全运行生产4年7个月,实现了长周期安全生产。     

基于序的原油调合调度问题

原油在线调合调度对于平稳混炼原油性质、保障先进控制的实施和优化炼油计划具有重要的意义。原油调合调度问题由于既包括顺序变量又包括连续变量,不存在明确的问题结构,复杂度高,传统的优化方法效率低,不能满足在线实时的要求。本文针对问题的两层结构提出了基于序的求解方案,利用实际原油性质数据的仿真结果表明,基于序的求解算法可以大幅度提高计算效率。     

基于流程模拟的常减压装置过程操作与生产计划集成优化

传统的炼油企业生产计划优化与过程操作优化往往是分离的, 从而造成生产计划优化系统制定出的生产方案可能在实际的生产装置操作上无法实现的情况。为了确保石化企业生产计划制定的方案可行的同时实现过程装置操作优化, 基于流程模拟软件建立了常减压蒸馏装置生产计划与过程操作的集成优化策略, 并提出了该优化策略的有效寻优方法。该方法通过流程模拟软件验证生产计划的可达性, 不断修正生产计划关键变量的优化区间, 在求得生产计划最优解的同时确定装置的工艺操作条件。以某炼油厂常减压蒸馏装置炼油为例验证提出的集成优化方法, 案例证明该集成策略不仅确保生产计划在实际生产的可操作性, 还得到了生产计划与过程装置操作的同步优化。     

集成炼油企业生产与能量系统的生产计划优化

炼油企业通常由物料生产系统与能量系统组成。传统的企业生产计划优化通常以生产系统物流优化为主, 能量系统基于物料生产优化结果进行产能优化以满足企业级能源供需平衡。此种优化方法不仅压缩了企业整体优化空间并且降低了燃料油与瓦斯等中间产品的利用率。基于装置能耗模型与能源供需以及中间产品的产耗质量平衡关系, 建立炼油企业生产系统与能源系统的集成优化模型, 通过求解MINLP模型进而实现企业生产物流与能流的集成优化。案例实践表明, 相较于传统分步优化方法, 集成优化方法不仅有效降低了全厂的生产成本, 并且实现了生产的节能减排。     

惠炼焦化石油焦灰分高的分析和解决措施

针对惠州炼油石油焦灰分偏高情况,惠州炼油1200万吨/常减压装置作为龙头装置,从加工原油情况进行针对性分析,确认原油中钙和镁对焦化石油焦灰分有较大影响,其它无机物例如原油泥沙或者焦化注剂也有一定影响;同时做好原油优化、原油电脱盐操作优化以及脱钙剂试用,有效降低石油焦灰分。     

基于敏感度分析的常减压蒸馏装置优化

通过对某炼油厂常减压装置模拟和分析,以各线产品均能满足质量要求的总拔出率为优化目标,结合塔的水力学计算和泵的核算,提出了基于敏感度分析的常减压装置优化策略.以此为指导,对该装置加工科威特原油200万t/a流程进行了优化.     

Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process

It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator — the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution.     

A novel model for multi-plant mixed heavy crude oils refinery planning

In this paper, multi-refinery using the same heavy crude oils as raw materials is studied, while a new nonlinear model for mixed heavy crude distillation is proposed. In practical crude distillation operation, the distillate yield and product distribution of distillation units are different due to their various equipment and operating parameters, even the same ratio of raw materials is provided, so different process models for multi-refinery planning is therefore required. For process modeling, the relationships between total yields and mixing ratio of different refineries were determined, which is combined with process simulation using production data. Then, the yields and properties of crude distillation unit (CDU) fractions were calculated with the use of true boiling point (TBP) curves and property curves respectively when the initial cutting temperatures were given. Finally, in order to maximize the economic benefit of distillation, the optimal product distribution and the best mixing ratio of crude oil were calculated under the constraints of different properties of fractions. Comparing to previous models, the proposed model takes the influence of different refinery parameters on production process into account, while avoiding the complex process for determining the cutting points, which is considered more effi-cient and more accurate with respect to heavy crude refinery. Model was successfully verified by a case study, allowing a significant improvement of the refinery profit to be achieved.     

Product tri-section based crude distillation unit model for refinery production planning and refinery optimization

Accuracy of a crude distillation unit (CDU) model has a significant impact on refinery production planning. High accuracy is typically accomplished via nonlinear models which causes convergence difficulties when the entire refinery model is optimized. CDU model presented in this work is a mixed-integer linear model with a modest number of binary variables; its accuracy is on par with rigorous tray to tray CDU models. The model relies on the observation¹² that a line through the middle of the product true boiling point (TBP) curve depends on the crude feed properties and the yields of the adjacent products. Novelty of the product tri-section CDU model is that it does not require models of individual distillation towers comprising the CDU, thereby leading to a much simpler model structure. Significant reduction in the computational effort required for the optimization of nonlinear refinery models is illustrated by comparison with previous work.     

Simultaneous optimization of heat-integrated crude oil distillation systems☆

Crude oil distil ation is important in refining industry. Operating variables of distil ation process have a critical ef-fect on product output value and energy consumption. However, the objectives of minimum energy consumption and maximum product output value do not coordinate with each other and do not lead to the maximum eco-nomic benefit of a refinery. In this paper, a systematic optimization approach is proposed for the maximum an-nual economic benefit of an existing crude oil distil ation system, considering product output value and energy consumption simultaneously. A shortcut model in Aspen Plus is used to describe the crude oil distillation and the pinch analysis is adopted to identify the target of energy recovery. The optimization is a nonlinear program-ming problem and solved by stochastic algorithm of particle warm optimization.     

炼油企业供应链管理中实沸点切割点的优化

20世纪90年代以来,国际石油和化工制造业面临新的挑战．企业要想提高竞争力,必须在产品成本、质量及新产品开发能力、生产灵活性以及用户的售后服务等方面取得优势．为此,应将企业整个生产过程放到供应链中去,使整个企业的原料供应→原料运输→仓储→生产制造→产品储存→产品运输→用户需求反馈等形成一个有机联系的系统（图1）．提高整个企业的生产和销售效率,就是提高供应链各环节的效率,对供应链进行动态优化,将所需数量和质量的产品,以最快的速度送到客户的手中．     

炼化一体智能化工厂计划优化模型设计及应用实践

炼化企业供应链协同优化对实现智能炼厂至关重要,计划优化系统是供应链协同优化实现的核心系统。本文详细介绍了规划总院自主研发的新一代计划优化软件整体框架及主要功能,在该平台基础上结合炼化一体化企业流程数据、原油数据库及产品销售等边界条件进行了炼化一体化模型设计及建设,用于开展各类优化分析,并基于线性规划原理,通过边际效益分析及多方案对比分析,结合原油保本价法、缩减成本法及等量替代法等不同的原油选购方法,开展了原油选购测算及应用分析,并对炼化一体化企业加工原油提出了相关建议,指导企业原油选购。     

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine(SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization(GA-PSO)method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.     

原油蒸馏塔的内外不可行路径优化方法

针对原油蒸馏塔的特点 ,将蒸馏塔模拟的内外法与优化的不可行路径法结合 ,提出原油蒸馏塔的内外不可行路径法 .对某原油蒸馏塔的优化计算表明该方法能提高计算速度 ,并取得较好的优化效果 .     

原油蒸馏塔的模拟计算

在深入研究石油馏分虚拟组分切割方法的基础上，按照原油常压蒸馏塔的工艺原理，提出了联合使用BP法和SR法，以功能模块组合的流程模拟法求解原油常压蒸馏塔的新途径。它具有收敛性好、计算精度高等特点，为进一步实现原油常压蒸馏塔的优化和炼油厂全流程的模拟与优化奠定了基础。